Predicting pedestrian trajectory is an essential task for accomplishing mobility-based jobs in naturalistic environments, such as robotics navigation in crowded areas, safe autonomous driving and many other applications that require foreseeing motion in an interactive dynamical system. Existing literature has focused so far on modeling the social interactions between pedestrians by discretizing the environment as a grid of local spatial neighborhoods [1]}, taking global scope of the whole scene [2]} or inferring relationships between pedestrians pairwise [3]}. So far, spatial neighborhoods have been the fundamental basis for considering pedestrians influence on each other, accounting only for the positional and higher-order motion features.
Other methods have used additional features such as head pose [4]}, [5]}, [6]} to attain the visual field of attention in pedestrians in order to assess how they are related to each other.
